Image alignment is fundamental to the functioning of our proposed geometry kernel, which measures zero-mean deviations in keypoint location within a common spatial reference frame. We thus evaluated prediction following two different subject-to-atlas registration methods: the original 3D rigid transform provided by the HCP mapping images to the standard MNI atlas using the FLIRT algorithm [1]}, [2]}, and feature-based alignment (FBA) [3]} using keypoint correspondences to estimate a 3D similarity transform mapping images to a subject atlas, for 20 different randomly selected atlases. All alignment solutions were very close to the reference MNI alignment (rotation differed by \(4.31^{\circ }\pm 2.84\) , translation by \(3 mm \pm 3.5\) ), reflecting a degree of inter-subject variability given different atlases. Most notably, however, was that AUC values were virtually identical for all prediction trials, with a maximum standard deviation of \(\epsilon = 0.0017\)  (see Table REF ), indicating that prediction was not sensitive to the alignment solution used. The results we report are thus consistent with the reference MNI alignment provided with the HCP data.
